Paper Type

Short

Paper Number

PACIS2026-1367

Description

This study examines how AI-generated background music shapes cognitive task performance in digital work environments. Drawing on arousal-mood theory, creator-attribution research, and the speed-accuracy trade-off, we conceptualized AI-generated music as a socio-cognitive artifact whose effects depend on both acoustic features and perceived authorship. We propose a 2 × 2 between-subjects experiment that manipulates musical tempo (low versus high) and creator identity (human-generated versus AI-generated). Task effectiveness is measured through arithmetic accuracy, whereas task efficiency is measured through response time. We further theorized that excitement and calmness mediate the effects of tempo and creator identity on performance outcomes. By distinguishing effectiveness from efficiency, this study explains why background music may simultaneously support and impair cognitive performance. The findings are expected to contribute to IS research on generative AI, human-AI interaction, and adaptive productivity systems.

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Jul 5th, 12:00 AM

AI-Generated Music and Cognitive Task Performance: The Role of Tempo and Affective States

This study examines how AI-generated background music shapes cognitive task performance in digital work environments. Drawing on arousal-mood theory, creator-attribution research, and the speed-accuracy trade-off, we conceptualized AI-generated music as a socio-cognitive artifact whose effects depend on both acoustic features and perceived authorship. We propose a 2 × 2 between-subjects experiment that manipulates musical tempo (low versus high) and creator identity (human-generated versus AI-generated). Task effectiveness is measured through arithmetic accuracy, whereas task efficiency is measured through response time. We further theorized that excitement and calmness mediate the effects of tempo and creator identity on performance outcomes. By distinguishing effectiveness from efficiency, this study explains why background music may simultaneously support and impair cognitive performance. The findings are expected to contribute to IS research on generative AI, human-AI interaction, and adaptive productivity systems.